Currently, a number of mathematical or statistical modeling methods may be used to predict a most efficient route between two known points of a network, according to one or more algorithms, functions or techniques. Such methods, which are sometimes called “shortest path” methods, are effective when predicting routes for vehicles having similar and substantially nominal capacities (e.g., operating speeds, delivery capacities, available power and fuel sources) to travel within a modern, reliable ground-based network. In particular, such methods may predict an optimal path for travel by automobile between two known points, viz., an origin and a destination, by representing the origin and the destination, and, optionally, one or more intervening waypoints, as nodes (or hubs) in a network. Estimates of costs or time for travel between the origin and the destination within the network may be obtained where intrinsic and extrinsic factors associated with such travel may be predicted with acceptable degrees of accuracy or precision.
Most path modeling systems and methods are ineffective or unhelpful, however, in environments where transportation networks include a number of non-traditional features. For example, while path modeling techniques may effectively predict a time for travel by automobile on a roadway network, or by train on a rail network, such techniques are less effective at predicting times for travel using vehicles such as bicycles, carts or robots that may travel on a variety of different surfaces or along a variety of different paths, including not only paved or unpaved roads but also on sidewalks or trails, or across lawns, plazas, parks, or the like, regardless of whether such surfaces or paths are component parts of paths of an established, static network.
Additionally, most path modeling techniques are typically unable to quickly adapt to changes in physical structure or architecture of any of the paths of a network, including the availability of new paths or the unavailability of previously existing paths, or to update predictions of optimal paths accordingly. At best, the only variables considered by typical path modeling techniques in selecting a route between two points in a network are prevailing or predicted travel times or speeds along paths of the network. Travel times or speeds are symptoms of real-time, unpredictable maladies such as weather conditions, accidents or congestion within the network, however, and are neither indicative of the actual maladies themselves nor determinative as to when such maladies may subside or otherwise be resolved.
Moreover, traditional path modeling techniques also fail to consider operational or functional capacities of a vehicle, such as standard or maximum operating speeds, power levels, ranges or other factors, when selecting or recommending a route between two points of a network or predicting a travel time between such points. Such techniques typically assume that all vehicles will travel at the maximum allowable speeds on paths within a network, as defined or capped by any regulatory limits such as speed limits, and do not consider whether or when a vehicle may run out of fuel or otherwise reach a maximum range. In this regard, traditional path modeling techniques treat a sedan in the same manner as a motor scooter or an eighteen-wheeler, despite their substantial differences in available power or carrying capacities, and assume that each of such vehicles has access to a limitless supply of fuel. Likewise, because traditional path modeling techniques do not consider the specific attributes of a vehicle when selecting or recommending a route or predicting a travel time, such techniques further fail to consider whether a given path of a network may accommodate a given vehicle, i.e., whether the dimensions and mass of the vehicle will actually fit on the path, as most vehicles on the road today have common dimensions such as widths.